Tracking and data association
Mobile Robot Localization and Map Building: A Multisensor Fusion Approach
Mobile Robot Localization and Map Building: A Multisensor Fusion Approach
Exploring artificial intelligence in the new millennium
A Discussion of Simultaneous Localization and Mapping
Autonomous Robots
Multi-robot Simultaneous Localization and Mapping using Particle Filters
International Journal of Robotics Research
Fast and accurate map merging for multi-robot systems
Autonomous Robots
A comparison of loop closing techniques in monocular SLAM
Robotics and Autonomous Systems
Multi-robot visual SLAM using a Rao-Blackwellized particle filter
Robotics and Autonomous Systems
Event-driven loop closure in multi-robot mapping
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Large scale multiple robot visual mapping with heterogeneous landmarks in semi-structured terrain
Robotics and Autonomous Systems
Hierarchical SLAM: Real-Time Accurate Mapping of Large Environments
IEEE Transactions on Robotics
The M-Space Feature Representation for SLAM
IEEE Transactions on Robotics
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This paper presents a multi-robot simultaneous localization and map building (SLAM) algorithm, suitable for environments which can be represented in terms of lines and segments. Linear features are described by adopting the recently introduced M-Space representation, which provides a unified framework for the parameterization of different kinds of features. The proposed solution to the cooperative SLAM problem is split into three phases. Initially, each robot solves the SLAM problem independently. When two robots meet, their local maps are merged together using robot-to-robot relative range and bearing measurements. Then, each robot starts over with the single-robot SLAM algorithm, by exploiting the merged map. The proposed map fusion technique is specifically tailored to the adopted feature representation, and takes into account explicitly the uncertainty affecting both the maps and the robot mutual measurements. Numerical simulations and experiments with a team composed of two robots performing SLAM in a real-world scenario, are presented to evaluate the effectiveness of the proposed approach.